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Title: How Generative AI Is Changing Creative Work

Author: Thomas H. Davenport and Nitin Mittal

Link: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work

Technical Dimension: Labor

Key Takeaways:

To use generative AI effectively you still need human involvement at the beginning and end of process (human needs to enter a prompt into a model --> creative prompt = creative output)

Humans are still needed to revise and edit work produced by AI

Workers have more time to do better research and strategize their marketing better since less time is spent on content creation

Generative AI creates new sets of opportunities: automated content generation, improved content quality, increased content variety, personalized content based on user preferences

AI has substantial impacts on marketing, design, entertainment and interpersonal communication

Quote: "AI is 10% and I am 90%" (there is still a lot of human involvement)

Questions:

Is more pressure placed on the human workforce when there is an expectation to do more with the new "free time" created do to increased productivity from using AI for content creation?

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Generative AI models for businesses threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. These models are able to produce text and images: blog posts, program code, poetry, and artwork. The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. Companies need to understand how these tools work, and how they can add value.

Large language and image AI models, sometimes called generative AI or foundation models, have created a new set of opportunities for businesses and professionals that perform content creation. Some of these opportunities include:

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How Generative AI Is Changing Creative Work

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Generative AI models for businesses threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. These models are able to produce text and images: blog posts, program code, poetry, and artwork.

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Title: Democratization and generative AI image creation: aesthetics, citizenship, and practices

Author: Maja Bak Herrie, Nicolas René Maleve, Lotte Philipsen and Asker Bryld Staunaes

Link: https://link.springer.com/article/10.1007/s00146-024-02102-y

Technical Dimension: Democratic

Key Takeaways:

There are arguments made for generative AI's use for propaganda, spread of disinformation, perpetuation of discriminatory stereotypes, and issues with authorship, authenticity and originality

AI offers people the opportunity to produce imagery that captures their intent without needing any artistic ability

Creating and distributing images contributes to public discourse (visual citizenship)

Fear of loss of credibility exists when using AI tools in news media (can be seen as unreliable without the "human" aspect attached to it)

Suggestions made:

develop software to distinguish generative AI images from photographs

implement mandatory watermarks/declarations of AI

industry certification of image agencies guaranteeing "human-made" photography

Access to generative AI tools is not universally granted --> affordability AND 'who' the product is designed for is at play (inherent biases)

User skill created a hierarchy among users in public discourse

Questions:

Does the fact that a hierarchy is possibly created among users based on skill make the benefit of everyone having a say in visual citizenship void?

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Democratization and generative AI image creation: aesthetics, citizenship, and practices - AI & SOCIETY

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The article critically analyzes how contemporary image practices involving generative artificial intelligence are entangled with processes of democratization. We demonstrate and discuss how generative artificial intelligence images raise questions of democratization and citizenship in terms of access, skills, validation, truths, and diversity. First, the article establishes a theoretical framework, which includes theory on democratization and aesthetics and lays the foundations for the analytical concepts of ‘formative’ and ‘generative’ visual citizenship. Next, we argue for the use of explorative and collaborative methods to investigate contemporary image practice, before analyzing the central part of our investigation, which takes the form of four collaborative workshops conducted in 2023 with external partners in different domains (the art scene, art therapy, education, and the news media). After analyzing insights from these workshops, the article significantly nuances how visual citizenship is at work in different manners depending on the different concrete image practices using generative artificial intelligence. Finally, we conclude that an aesthetic perspective offers valuable insights into foundational aspects of belonging to contemporary visual communities.

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Title: A.I. Is Going to Disrupt the Labor Market. It Doesn't Have to Destroy It.

Author: Rebecca Stropoli

Link: https://www.chicagobooth.edu/review/ai-is-going-disrupt-labor-market-it-doesnt-have-destroy-it

Technical Dimension: Labor

Key Takeaways:

There is an argument being made that jobs will be complemented by rather than lost to generative AI

A report predicts a 7% ($7 trillion) increase in global GDP and boost productivity growth by 1.5% due to generative AI

Creation of new jobs could help offset the losses

Journalists and web designers are on the list of professions most likely to be affected by generative AI

Workers who used generative AI for content creation at work were found to have decreased their time spent on work while increasing the quality of their work

If this type of AI ends up replacing rather than complementing workers it could decrease the demand for human labor and job opportunities

Questions:

How is job compensation/wage affected even if generative AI complements human labor rather than replacing it?

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The University of Chicago Booth School of Business

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Holzer, agreeing that the current US tax code favors displacement, also notes that he doesn't advocate any type of broad robot or A.I. tax, as it would be a tax on productivity growth.

He suggests instead a sort of displacement tax. "Every time you displace a worker because of A.I., they [companies] have to pay a tax on that," he says. "And if you're going to retrain them [employees], then maybe we subsidize that. So you use taxes to discourage displacement that isn't necessary, and you use the subsidy system to subsidize retraining. You want there to be the right incentives."

This matters at two levels-for the companies developing A.I. and for those adopting it-he says. "You want their incentive to be less toward pure replacement and more toward human-centered A.I."

To that end, companies can strive to invest in A.I. that as much as possible augments rather than replaces the complex tasks that human workers can do. As Autor, Chin, Salomons, and Seegmiller conclude in their research, when technology augments rather than displaces human workers, we see more valuable new work created, labor demand boosted, and wages raised.

Governments can take the lead in funding A.I. research in "areas where A.I. can create new tasks that increase human productivity and new products and algorithms that can empower workers and citizens," notes Acemoglu in additional research.

The Biden administration in 2023 indeed announced several measures to promote responsible A.I. innovation, including allotting $140 million in funding for research and development and issuing a wide-ranging executive order addressing A.I. topics from safety to ethics to potential job loss. And in June, the European Parliament agreed on a draft of the A.I. Act, which could become one of the first all-encompassing laws regulating the technology. The act focuses largely on stemming risks such as privacy violations and hazards to health or safety, and it includes protection proposals for workers subject to company A.I. platforms that are used to hire and manage them.

The Partnership on AI, as part of its shared prosperity guidelines, suggests that companies adopting A.I.-enabled tech should do thorough evaluations to ensure systems align with worker needs. Among the steps they prescribe: secure systems that improve job quality while eliminating undesirable tasks, recognize any extra work created by A.I. and ensure it's acknowledged and compensated, create teams that test systems for misuse, establish transparency on worker data collection and use, and allow workers to opt out of those data practices.

Klinova says the Partnership on AI is working with unions, companies, and policy makers to refine, test, and encourage adoption of their shared prosperity guidelines.

2. Create opportunities for upskilling

In recent years, the idea of creating universal basic income programs to assist workers displaced by automation and A.I. gained some traction, including in the short-lived US presidential campaign of Andrew Yang. However, many economists prefer the idea of upskilling over passive income replacement. "Even if technology is diffusing fast," Humlum says, "I'm much more supportive of investing in people than just income supporting them."

University of Cambridge's Diane Coyle is even more dismissive of UBI, calling it "a chimera advocated by Silicon Valley individualists who don't want to take responsibility for the social consequences of their innovations." She adds, "Nobody can buy a transportation system or infrastructure or good public schools with an individual income."

Research suggests that reskilling programs can help those with lower-demand skills elevate their professional status. A 2022 White House report notes that government should be "investing in training and job transition services so that those employees most disrupted by A.I. can transition effectively to new positions where their skills and experience are most applicable."

In the US, Holzer points to successful implementation of sector-based training, in which people prepare for jobs in high-demand areas that offer good pay to workers without college degrees.

Typically, an intermediary organization with knowledge on a specific industry brings training providers-most often community or technical colleges-together with employers and industry associations, providing support and services to disadvantaged students.

Harvard's Lawrence F. Katz, Brown's Jonathan Roth, and Richard Hendra and Kelsey Schaberg from the research organization MDRC studied several sector-based programs that focused on workers in industries including manufacturing, healthcare, transportation, and IT. They find that the programs (including WorkAdvance, Project QUEST, and Year Up) led to earnings gains of 14-38 percent in the year following training completion. Additionally, they find, earnings gains persisted for at least several years "with little evidence of the fade out of treatment impacts found in many evaluations of past employment programs."

So far, Holzer says, these programs have been more for disadvantaged than displaced workers, but the model could work for employees who lose work due to A.I.

"You can imagine a worker who's displaced, say in healthcare or in IT, that you might be able to retool them more quickly if it remains a growing field," he says. And the place this should be happening most is at the community-college level, he suggests. But, he notes, while community colleges have the scale, lack of support-for one, they are given far fewer public subsidies than four-year colleges-is a major challenge to program quality.

Booth's Humlum, through research with University of Copenhagen's Jakob R. Munch and UCPH PhD student Pernille Plato Jørgensen, studied the effects of a reskilling program in Denmark for workers injured on the job. Workers were transitioned from physical to more cognitive occupations, and while this program focused on displacement due to injury, the researchers note it could potentially translate for workers displaced through automation.

The study followed workers who instead of receiving disability payments from the government took the same amount of money to either enroll in formal education courses or participate in an on-site training program. According to the study, the most common choice for those who enrolled in classroom training was a four-year bachelor's degree that transitioned their physical work to more knowledge-based work in the same field-for example, a former construction worker would enter a degree program in construction architecture. Of those workers, 80 percent found new employment within seven years of their accidents, and on average earned 25 percent more than before they were injured.

Another study Humlum conducted in Denmark, with Munch and UCPH's Mette Rasmussen, finds similar advantages to upskilling, which the researchers argue is more beneficial than on-the-job training programs. While their study focused largely on workers displaced due to offshoring, the key result could translate to job loss or downsizing due to A.I. and automation. The researchers find that unemployed workers whose jobs were at the most risk for offshoring and who were assigned to classroom training-which allowed them to learn new skills that could help them switch occupations-had gained, on average, 55 hours of work per month compared with their previous employment.

Similarly, the United Kingdom in 2017 announced the National Retraining Scheme program, eventually investing £100 million to train workers, particularly those displaced by A.I. and automation. And US companies are also taking notice. Amazon's Upskilling 2025 program, for one, is investing $1.2 billion to provide employees access to training programs that prepare them for higher-level jobs.

"I do think reskilling will become increasingly important," Humlum says, "just because technology is diffusing faster . . . and we are living longer, and those two facts combined mean that we have to confront more technologies during one single career." In their research, Humlum, Munch, and Rasmussen note that while Denmark spends as much as 2 percent of its GDP on active labor-market policies, the US emphasizes more passive programs such as unemployment and disability insurance.

The White House report notes that "the increased prevalence of shorter contract durations lowers incentives for firms to invest in worker training," and that it may therefore be useful to subsidize intermediaries such as public employment services and temporary-employment agencies to share the costs and benefits of training.

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A.I. Is Going to Disrupt the Labor Market. It Doesn't Have to Destroy It.

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The decisions of companies, governments, and educators will help to shape the ultimate outcomes of the A.I. revolution.

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Title: AI Is Dangerous, but Not for the Reasons You Think

Author: Sasha Luccioni

Link: https://www.youtube.com/watch?v=eXdVDhOGqoE

Technical Dimension: Democratic

Key Takeaways:

Difficult for artists/authors to prove their work has been used for training AI models without their consent

Software has been created by artists to determine whether their work has been used without their consent (allows them to sue companies for copyright infringement)

AI models encode patterns and beliefs that can represent stereotypes or racism/sexism

Image generation models have been found to represent the same biased idea of something (ex: significant representation of whiteness and masculinity across professions)

Questions:

What are ways to combat the biases encoded into AI generated content?

How can you decipher on the surface whether content created by AI is based on stereotypes/biases?

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AI Is Dangerous, but Not for the Reasons You Think | Sasha Luccioni | TED

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Title: What Will Happen to Marketing in the Age of AI?

Author: Jessica Apotheker

Link: https://www.youtube.com/watch?v=3MwMII8n1qM

Technical Dimension: Labor

Key Takeaways:

Generative AI in content creation is the next big productivity revolution

Marketing excels by leaning to the emotional needs of consumers, developing a product to meet those needs, and creating a message that will convert the consumer

Generative AI is transforming the core of marketing activities

Study found that generative AI improved performances of marketers by 40%

Increases free time for employees

If the productivity revolution is steered correctly, marketers will invest their increased free time in more content/ideas

More content = more personalized content as consumers

Negative outcome: content overload

Generative AI reduces divergence of outcomes because it is trained on specific data

Study found that when people over rely on generative AI the collective divergence of ideas drops by 40% (new ideas/innovation is stagnant)

Questions:

How should companies approach finding a balance between over reliance and enough reliance on AI to generate content while seeing increases in productivity?

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What Will Happen to Marketing in the Age of AI? | Jessica Apotheker | TED

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Title: A Matter of Self-Regulation: the Music Industry's Short-Term Response to AI's Legal Obscurity

Author: Vaughn Gendron

Link: https://business-law-review.law.miami.edu/a-matter-of-self-regulation-the-music-industrys-short-term-response-to-ais-legal-obscurity/

Technical Dimension: Legal

Key Takeaways:

AI generated voice models are used to create songs with likeness to certain artists

Results in millions of AI generated deepfakes

Issues results with copyright and right of publicity concerns due to the creation of deepfakes and using artists' voices

Arguments made that training AI voice models with unlicensed copyrighted music constitutes copyright infringement

Legal ways to fight the replication of an artist's voice is restricted since voices are not copyrightable, and vocal protections via the right to publicity differ from state to state

It has been requested that AI companies should be required to obtain licenses in order to use copyrighted music in the training of their systems

There has been some mention of developing a tool that will allow users to create AI generated songs through which the copyright owners get paid (artists have the choice to opt-in or out)

A self-regulation approach over legal action has been utilized (negotiations/partnerships directly between tech companies and music platforms)

Questions:

Does it seem as though the self-regulation approach rather than legal action will lead to more change with this specific issue? Or is it simply a temporary and small fix?

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A Matter of Self-Regulation: the Music Industry’s Short-Term Response to AI’s Legal Obscurity | Business Law Review

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Vaughn Gendron – In 1999, the music industry’s business model was turned upside down. With the introduction of Napster, copyrighted music was suddenly being shared and downloaded for free by millions of Internet users. This level of piracy led to a massive decline in music sales. However, thanks to copyright law, Napster was forced to settle for $26 million, which ultimately led to the company’s demise and an end to the widespread piracy.

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Title: Innovation and Artists' Rights in the Age of Generative AI

Author: Daryl Lim

Link: https://gjia.georgetown.edu/2024/07/10/innovation-and-artists-rights-in-the-age-of-generative-ai/#:~:text=Specifically%2C%20they%20criticize%20the%20training,that%20generative%20AI%20has%20disrupted.

Technical Dimension: Legal

Key Takeaways:

AI transforms datasets into "original content"

Often results in using copyrighted material without consent --> leads to potential infringement issues

AI threatens human creativity and devalues artists' rights

The earning of original creators are affected when AI generated content is published

AI is trained on copyrighted content without the owners' consent

Solutions:

Reconciling legislation

Limiting training data

Varied approaches to global AI governance

The fair use doctrine is used as a mode of defense by AI developers

US is considering the Generative AI Copyright Disclosure Act to protect artists' rights and copyrighted products

There are different global approaches to AI governance that are shaped by market practices and political power

Questions:

Who is liable and who has property rights in the potential case that AI uses previously generated music (that was initially trained through real music) to train the system to create new music? Real Music --> Generated Music --> Generated Music (who owns the second round of generated music if it was initially trained using an artists' content but then made using AI generated content)?

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Georgetown Journal of International Affairs

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Artists’ Rights in the Age of Generative AI | GJIA

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The rise of generative artificial intelligence (AI) in the music industry has sparked a significant debate among artists and AI developers.

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Title: Legal Issues with AI-Generated Content: Copyright and ChatGPT

Author: Dr. Maria Chetcuti Cauchi

Link: https://chambers.com/articles/legal-issues-with-ai-generated-content-copyright-and-chatgpt

Technical Dimension: Legal

Key Takeaways:

Literary copyright ensure creators have control over the use and distribution of their work

Provides legal safeguards against unauthorized use/reproduction/distribution/adaptation

AI software is subject to copyright laws

Copyright law only applies to things made by humans

AI generated content does not fall under copyright protection because it is produced by an algorithm rather than a human

AI has been used more commonly for creation production (presents issues regarding property rights and ownership of work)

Arguments made:

If copyright protection is not given to AI generated content --> encourages human creativity over machine creativity

If copyright protection is given to AI generated content --> places an equal value on human and machine creativity and favors quantity of work

Questions:

What does the current government system place a higher value on, human creativity and freedom of expression or machine generation and profit?

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Legal Issues with AI-Generated Content: Copyright and ChatGPT | Article | Chambers and Partners

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This Article gives an overview about “Legal Issues with AI-Generated Content: Copyright and ChatGPT”. Find out more on Chambers and Partners.

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Title: AI Impact: How AI is Transforming the Music Industry

Author: CNBC Television

Link: https://www.youtube.com/watch?v=E1wDOAmYHSs

Technical Dimension: Legal

Key Takeaways:

Google is partnering with record labels to create guardrails to protect artists' voices from being exploited and to allow them to profit through AI generating tools/music

Creators can make AI generated songs with artists' voices

Artists are being paid for participation of their voices

Artists need to decide whether they embrace this development or get left behind

Some artists are asking fans to make songs using their voices (ex: Grimes will share royalties with fans who create good songs)

Questions:

How will the legal progression be affected if some artists are encouraging the use of AI generated tools to create music by fans (like Grimes), while others are not?

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AI Impact: How AI is transforming the music industry

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Title: Record labels are suing AI music generators for copyright infringement

Author: NBC News

Link: https://www.youtube.com/watch?v=tKEEda_OHj0

Technical Dimension: Legal

Key Takeaways:

Lawsuits have occurred of artists/labels accusing companies of training AI models using copyrighted music without their permission

Question that arises: who owns this property if it sounds like someone's voice but was created by AI? (who is liable)

Some AI companies claim that it's fair to copy and artist's work and exploit it for their own profit without consent or pay

Opens up a conversation on whether it is the responsibly of the record labels or AI companies to prove they have the right to use the artists' voices

"Is it the exact same thing or 'artistic representation'"

Questions:

Why are companies arguing that it is within their rights to exploit other artists' work for their own profit without giving credit to the artists? What are the arguments for this process being seen as okay?

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Record labels are suing AI music generators for copyright infringement

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Title: Insights on Generative AI and the Future of Work

Author: North Carolina Department of Commerce

Link: https://www.commerce.nc.gov/news/the-lead-feed/generative-ai-and-future-work

Technical Dimension: Labor

Key Takeaways:

AI can potentially influence jobs that were previously thought to be immune to automation (ex: jobs that require creativity)

Jobs at risk: white-collar/professional jobs

Typically require higher education and offer higher pay

Jobs not at high risk: physical and/or outdoor jobs

AI adoption is influenced by: willingness of employees to integrate AI into their workflow + skills needed to use AI tools

Can increase job satisfaction (automate mundane tasks and allowing employees to focus on meaningful work)

Can enhance the quality and efficiency of work

Less-experienced workers might be at an advantage in terms of productivity if they learn to use AI tools

Limitations:

Biases in outputs due to biases in the training data

Producing unpredictable or false information

Questions:

If AI adoption is influenced by employees' willingness to incorporate it into their work, is it not possible to hold back the incorporation of AI and its effects if numerous employees refuse to use it?

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Insights on Generative AI and the Future of Work

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After LEAD published our first article discussing ChatGPT, we have continued to learn and develop new insights that reshape our understanding of generative artificial intelligence (AI) and the workforce. Here are the top five things we have learned so far.

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Title: Tommie Experts: Generative AI's Real-World Impact on Job Markets

Author: Manjeet Rege

Link: https://news.stthomas.edu/generative-ais-real-world-impact-on-job-markets/

Technical Dimension: Labor

Key Takeaways:

Critical for content production, event highlighting and content management in the media/entertainment sector (enhance creative and decision-making processes)

Has the potential to influence job displacement and increasing the need for new skills for employment

Media/entertainment is an industry most vulnerable to the impact of generative AI (content creation/editing/management can be streamlined by AI tools)

Women are disproportionately affected by the AI's displacement

Men are more evenly distrusted across white/blue collar professions, but not women

Black individuals are overrepresented in jobs that are at high risk for automation due to AI

Increasing skills in the workforce is needed to cut down on the disproportionate effects on marginalized groups

Policies need to be enacted to ensure equal access/division of the advantages from AI tools

Questions:

How would increasing skills on using AI cut down on the disproportionate effects on marginalized groups if displacement is a result of the presence of automation rather than the lack of technical skills?

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Tommie Experts: Generative AI’s Real-World Impact on Job Markets - Newsroom | University of St. Thomas

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Generative artificial intelligence is now an increasing concern, with talk about its potential and concerns about job displacement.

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Title: The impact of generative AI in a global election year

Author: Valerie Wirtschafter

Link: https://www.brookings.edu/articles/the-impact-of-generative-ai-in-a-global-election-year/

Technical Dimension: Democratic

Key Takeaways:

Threat to democratic discourse and integrity due to fake/manipulated generated content

Easier to mass produce and spread fake information that is of higher quality and hard to tell apart from the truth (less costly)

Issues created:

Generating depictions of people/events that are false and difficult to debunk

Result in authentic information being seen as false/generated to avoid questions regarding accountability

Difficult to know how frequently generated content spreads fake information across the internet due to diversity of channels for distributing content and difficulty in identifying generated content

Case study: deepfake influenced Slovakia's pre-election

Deepfakes are used to put governmental individuals at risk in scandals

Deepfakes can sway public opinions and increase political divisions

Easier to discover on a national level, but harder on a more local level where resources/attention is limited

Possible interventions to combat AI issues:

Legislative measures targeting deepfakes

Voter education initiatives

Tech companies should watermark content and create tools for detecting fake content

Questions:

How are deepfakes currently targeted and removed? What policies or softwares are currently available?

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The impact of generative AI in a global election year | Brookings

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If not better regulated, generative AI may disrupt contested information spaces and undermine democratic processes around the world in 2024.

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Title: Navigating Generative AI and its Impact on the Future of Public Discourse

Author: Hangyu Fan

Link: https://cheme-seas.ias-drupal7-content.cc.columbia.edu/news/navigating-generative-ai-and-its-impact-future-public-discourse

Technical Dimension: Democratic

Key Takeaways:

Disinformation as a threat to democracy

A diverse curation of sources is needed to enhance the factual accuracy of generative AI systems and the information they output

There is a gap between the AI systems and our trustworthiness of them

Important to develop systems that can identify AI generated content, and to teach people how to spot fake content

Questions:

What are the ways in which people can spot fake content? Are there specific characteristics to look out for?

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Navigating Generative AI and its Impact on the Future of Public Discourse

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Columbia Engineering and the Knight First Amendment Institute recently convened multidisciplinary experts to discuss the impact of artificial intelligence on public discourse, free speech, and democracy.

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Title: Generative AI in Political Advertising

Author: Christina LaChapelle, Catherine Tucker

Link: https://www.brennancenter.org/our-work/research-reports/generative-ai-political-advertising

Technical Dimension: Democratic

Key Takeaways:

Used to create ads and fundraising initiatives

Positives:

Reduces the need for large teams (better for campaigns that do not have as many resources)

Inexpensive

Limited training needed

1) target specific audiences 2) empower less-resources campaigns 3) improve ad effectiveness

Negatives:

Risk of misleading people

Increasing misinformation

1) produce false/misleading content 2) "hallucinate" information to fill in the gaps of AI knowledge 3) lack of internal consistency 4) biases

Questions:

Wouldn't the reduced need for a large team be a negative considering it means less people are hired/people are fired?

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View the entire AI and Democracy series

As the 2024 U.S. elections gain momentum, political campaigns and advertising agencies are turning to a new ally, artificial intelligence, to disseminate their messages. This emerging technology presents both risks and potential benefits for the political advertising industry. AI-powered tools can generate new text, images, video, and speech from a single prompt to weave into campaign messages. Political campaigns are already using these tools to create messages for ads and fundraising solicitations. AI software can even compose campaign emails. Generative AI is poised to redefine modern campaigning, although the exact nature of its influence remains uncertain. However, despite calls for regulation or moratoria in Congress, the Federal Election Commission, and even the political consultants' trade association, national lawmakers have yet to address the new technology.

New AI software products are inexpensive, require almost no training to use, and can generate seemingly limitless content. These tools can support personalized advertising at scale, reducing the need for large digital teams and leveling the playing field for campaigns that lack substantial resources. Yet AI also introduces a novel set of challenges, from a tendency to generate bland and repetitive text to the risk of misleading audiences and amplifying ongoing election misinformation issues. This essay examines ways in which AI - and specifically large language models that generate text - can enhance campaigns' voter outreach efforts, the cautions that campaigns must exercise when embracing new AI tools, and the market forces affecting both the technology itself and campaigns' experience as generative AI consumers.

We focus primarily on considerations that could influence the decision-making of campaigns themselves. Other essays in this series delve into broader threats to democracy associated with AI use, like the disturbing trend of AI-generated deepfakes in campaigns and AI as a tool for voter suppression, along with policy responses to these problems. Here, we take a campaign's-eye view of some of the strengths and weaknesses of generative AI in political advertising and the likely course that the market will take.

How Can AI Make Political Advertising More Powerful?

Throughout history, political campaigns have evolved in tandem with new media platforms, from the rise of radio broadcasting in the 1920s to the proliferation of the internet and social media in the 21st century. In recent years, data analytics and microtargeting tools have become the bedrock of modern campaigning. Campaigns now rely on large data sets that offer detailed insights into citizens' behaviors, interests, and whereabouts in order to advance their key goals, such as voter mobilization and fundraising. Yet using this wealth of data to deliver the right message to the right person at the right time demands considerable labor and expertise, as well as precision tools. And this data-driven targeting is not perfect: campaigns sometimes end up delivering the wrong message or targeting the wrong person. AI has the potential to make data-driven tools even more powerful and accessible.

Targeting Specific Audiences

From a campaign perspective, AI's ability to synthesize information about a target audience and generate a persuasive message tailored to that audience's interests holds great promise for microtargeting efforts. Free AI tools, such as OpenAI's ChatGPT, Microsoft's Bing, and Google's Bard, are each capable of producing relevant, comprehensive, and sophisticated marketing copy for a target audience. What's more, such tools can accomplish this task on a massive scale. AI can fine-tune messages for a diverse array of voter groups and their subgroups, and it can execute these refinements hundreds if not thousands of times daily.

For a political campaign wanting to address the unique concerns of different voters - such as women worried about reproductive health care costs, young voters newly participating in the democratic process, parents unsure about educational opportunities for their children, or rural communities facing problems with limited infrastructure - AI can feasibly assist in developing and targeting messages that address all these concerns and more. Indeed, this year, major platforms like Meta and Google have begun implementing AI-powered tools for advertisers that aim to make the personalization of ad messages easier and more efficient.

Empowering Less-Resourced Campaigns

AI can be especially helpful to political campaigns with fewer financial resources. Well-funded, high-profile campaigns can usually afford sizeable digital teams capable of disseminating a large volume of targeted ads; smaller, less moneyed campaigns have historically been unable to compete in this area. The current AI landscape, with its multitude of low-cost and user-friendly tools that require no prior knowledge of coding or machine learning, disrupts that equation. Low-resource campaign teams can harness this accessibility going forward and outsource targeted advertising production to these tools. Furthermore, AI-generated content can rival the sophistication of big-budget campaigns, giving smaller campaigns a boost in competing with larger ones.

Improving Ad Effectiveness

AI's adroitness at tailoring messaging could make ads more effective at engaging audiences. Although recent academic work has cast doubt on how good political campaigns actually are at persuading voters to support particular candidates or initiatives, the bulk of digital campaign efforts lie in mobilizing supporters to perform certain actions, such as donating or turning out on Election Day. AI's ability to generate well-reasoned political arguments tightly aligned with a target audience's preferences stands to improve the overall effectiveness of such political microtargeting efforts. Indeed, some early evidence suggests that voters find AI-generated microtargeted ads fairly convincing and even favor AI-crafted political arguments over human-crafted ones, primarily due to AI's ability to generate easy-to-read, fact-driven arguments that are more positive in tone.

What Are the Risks of AI Use in Political Advertising?

The power of generative AI to create content without humans comes with significant pitfalls. For one, the very features that enable campaigns to deliver targeted and effective messages at scale and on a budget could, in the hands of bad actors, be used in ways that imperil democracy, such as to suppress voting or incite violence. Even for well-intentioned campaigns, unsupervised AI can produce unoriginal, biased, or inaccurate messages.

Falsehoods and Empty Promises

Much of the media debate surrounding artificial intelligence in politics focuses on AI's potential to generate false or misleading content. In June 2023, headlines reported on Florida Gov. Ron DeSantis's presidential campaign sharing fake AI-generated images (sometimes called deepfakes) depicting Donald Trump embracing former Covid czar Anthony Fauci. Amplifying these concerns is AI's swiftly advancing ability to replicate reality, which makes it increasingly challenging for voters to distinguish between AI-generated and human-generated material.

Given the current absence of AI regulations, there is growing apprehension that antidemocratic groups or other bad actors could exploit AI-driven advertising technology to unleash torrents of misinformation on the internet. This very real possibility underscores the urgency to establish robust detection systems that can rapidly spot manipulated or fabricated content and bring it to voters' attention.

From the perspective of well-intentioned campaigns, however, an issue that has received less attention in the press lies in AI's capacity to inadvertently create false content. Text generation tools like ChatGPT have been shown to "hallucinate," fabricating facts about individuals or events to fill in gaps in their knowledge. Recently, a mayoral candidate in Toronto used AI-generated images in a platform document, one of which depicted a person with three arms.

Even the most well-meaning campaigns must take care to ensure that AI-generated content avoids such mistakes, remains factually accurate, and aligns with their planned messages. Given AI's ability to churn out mountains of content, messages containing false information can easily slip through the cracks, particularly for low-resource campaigns lacking the staff to scrutinize every item generated by AI.

Campaigns also face the risk that AI will blur their core messaging. AI crafts ad messages that it judges will best fit with a targeted voter's or group's interests and preferences. As they undertake this process for various voter groups, AI tools are likely to emphasize different topics or issues and may even put forth different stances on those issues. Quite simply, unlike a human campaign team, AI does not have the same degree of internal consistency. This shortcoming proves especially problematic for campaigns striving to reach a diverse audience consisting of multiple voter groups with competing interests. The challenge escalates as the level of human oversight diminishes, potentially leaving a campaign unaware of all the claims and commitments propagated through its AI-generated ads during an election cycle.

Over time, voters may become more disillusioned with the electoral process as campaign promises made in AI-generated ads go unfulfilled. This could dampen turnout in future elections, with voters continually questioning the sincerity of any competing candidate. Even more concerning are cases in which an AI-generated message has directly asked for a donation and invented promises to obtain that donation. Such cases raise questions of fraud and pose the possibility that campaigns may face future legal repercussions for fundraising under false pretenses.

Biases

Relatedly, some political campaigns may struggle with biases inherent in AI systems. Given that AI is trained on historical, encyclopedic, or public data, AI systems absorb any and all biases present in this human-generated data. In particular, AI trained on data from the internet, which includes copious racist and sexist content (among other problems), is likely to generate racist and sexist messages. Moreover, several studies have illuminated political opinion biases in commonly used large language models. For instance, whereas ChatGPT often echoes left-wing libertarian views, Meta's LLaMA tends to lean toward a right-wing authoritarian perspective. This divergence poses a dilemma for campaigns aiming to ensure that AI-generated ad content resonates with specific political values, as they may be unaware of bias and unable to supervise AI outputs at scale.

Ignorance of Certain Topics

Another hurdle that campaigns might encounter stems from the limitations of the data on which AI tools are trained. According to one study, AI appeared to struggle to formulate arguments on political issues that are less discussed in public political spaces, such as organ donation, instead producing output consisting of unrelated issue stances or rudimentary sentence fragments. Another study found that nine of the most-used AI tools could not generate content that accurately reflects the political opinions of different U.S. demographic groups, despite additional prompting. This deficiency highlights AI's struggle with nuance, an important facet in politics: campaigns must be able to glean and respond to subtleties in the electorate's opinions.

Generic Language

Finally, a downside missing from the discussion of common AI alarm bells centers on originality in AI-generated political ads. When prompted to craft political ads, AI synthesizes the style, tone, and content of ads present in the data on which it was trained. This process tends to dilute distinctive, inventive, or attention-grabbing elements that make ads unique, resulting in ads filled with generic statements and lacking the freshness and uniqueness that campaigns often aspire to convey. This issue dovetails with the ongoing challenge of AI cannibalism, wherein AI is now learning from AI-generated content, perpetuating trends of repetitive, unoriginal language in its output. If these homogenized political ads end up inundating the feeds, websites, and channels that voters view, then campaigns may find that those ads struggle to capture and maintain voters' attention.

To illustrate these points, consider an example from one of the most widely used AI tools. We asked ChatGPT to produce an ad for social media featuring a fictional political candidate:

ChatGPT

The AI-generated ad copy exemplifies the following issues:

Generic statements. The ad contains generic political slogans like "a brighter future" and "together, we can make a difference" and is largely devoid of a unique voice or personality to resonate with the audience. These flaws demonstrate how personalization at scale might not be as powerful as it initially seems in certain contexts.

Made-up promises. The ad commits the candidate to initiatives that were not in the prompt, like creating local jobs and lowering prescription drug costs. Such promises run the risk of alienating voters if the candidate is unprepared or unwilling to fulfill these commitments down the road. In addition, for other prompts we tried, ChatGPT included nonexistent website URLs and social media handles in its ad copy - a big problem for campaigns wanting to direct the ad audience to actual websites or social media feeds.

Biased assumptions. Although the prompt's language is neutral, the generated text assumes a fairly liberal stance, framing a vote for the candidate as a "Vote for Progress" and policies like providing affordable health care and a pathway to citizenship for immigrants. Campaigns across the political spectrum must be wary of biases embedded within AI systems leaking into their messaging.

To be clear, these issues are not specific to ChatGPT or OpenAI. When we gave the same prompts to different AI tools, they produced remarkably similar-sounding political ads with comparable snags - generic statements, unprompted assumptions, and made-up promises. Predicting exactly what kinds of political ads these AI tools will generate in the future is impossible, but if AI continues to learn from itself, then these problems are not going away.

What to Do: Can Market Forces Help?

Other essays in this series address potential governmental responses to election-related challenges posed by AI. To be sure, regulation could ameliorate some of the risks of generative AI. For example, transparency rules (such as a requirement that AI-generated messages include a disclaimer) could warn audiences to watch for false or biased content. But public policy cannot solve some of the issues that threaten to curtail AI's usefulness for campaigns, especially the problem of generic text in AI-generated ads. Fortunately, certain market forces intrinsic to online advertising may actually be an effective remedy for the problem. The market forces we are thinking of are those that reflect the need of advertising platforms themselves to engage two groups: users and advertisers.

To understand how, we need to look at the economics underlying advertising-supported attention platforms. The term attention platform encompasses traditional ad-supported media (including radio, television, and print outlets) as well as online businesses like search and social media platforms. Attention platforms compete with one another by posting engaging content alongside ads that are as appealing as possible. Platforms compete for users' attention and want to avoid repelling them by having unappealing ads. We can see this impetus across many platforms, from YouTube's innovations in giving users control over video ads to Meta's transformation of its ads toward visual appeal.

In sum, to compete, attention platforms must present appealing ads that don't annoy their users. But they're also incentivized to promote the ads that deliver their advertisers the highest return on investment - typically those ads most effective at engaging the attention of users. In other words, online advertising systems reward ads that are more engaging.

Going forward, we expect this natural market phenomenon to reduce incentives for generic, mass-produced ad copy riddled with the biases and false promises that we have pinpointed as key concerns. Just as users have come to engage more with political ads that are more appealing, campaign advertisers will come to distribute ads that are more appealing, with and without the aid of generative AI.

The use of AI in political advertising is still in its infancy, and we anticipate developments in the market that will assist campaigns in producing more effective ads. We expect that the foundational models on which AI systems are built will become richer in the breadth and depth of data they use, hopefully leading to fewer hallucinations and less ignorance of underserved political groups. We also expect that campaigns and advertisers will insist on more control over the output of generative AI, leading platforms to introduce companion tools that allow users to set strict boundaries on campaign promises or 'fact check' AI-generated ad copy for errors.

For now, as the 2024 election looms and political campaigns are already employing AI in their messaging plans, they need to keep in mind the limitations of AI when using it extensively to connect with voters.

Heightened public awareness of the power of generative AI could give politicians an incentive to lie about the authenticity of real content.

Lawmakers need to both take advantage of AI advances and create needed safeguards.

AI tools could make it easier than ever for bad actors to wage campaigns aimed at suppressing votes.

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Political campaigns should be watchful of the risks and opportunities of using generative AI to engage with voters.